[CppAD] Goddard Rocket using CppAD and Ipopt

[Brad Bell]

If you look at the source code in
https://projects.coin-or.org/CppAD/browser/trunk/example/ipopt_cppad_nlp.cpp

You will notice that if retape is true then the sparsity patterns are set 
to dense. On the other hand, if retape is false, the sparsity patterns are 
calculated and used. This is because CppAD assumes that the operation 
sequence may change when retape is true.

If you are unsure if the operation sequence is changing, you can try using 
retape equal to false and true. If the answers are different, then the 
operation sequence is changing and you need to use retape equal to true.

You may be able change you code and avoid having to retape using 
conditional expressions.
 	http://www.coin-or.org/CppAD/Doc/condexp.xml

On Thu, 31 Jul 2008, Andreas Waechter wrote:

> Hi Jens,
>
> Brad is the best to comment on the performance of CppAD, I don't konw about 
> this.
>
> However, what I see is that the Jacobian and the Hessian are given to Ipopt 
> in DENSE representation!  I don't know how/if CppAD detects which constraint 
> functions depend on which variables, but from the output you sent it seems 
> that the information give to Ipopt is that all functions first and second 
> derivatives depend on all optimization variables.  This of course leads to a 
> huge increase in memory consumption and computation time compared to the run 
> with sparse matrices that your other implementation did.
>
.
.
.
>> On Thu, 31 Jul 2008, Jens A. Berger wrote:
>> 
>>> Dear Bradley and Andreas
>>> 
>>> using the "ipopt_cppad_nlp" class of the CppAD examples we implemented
>>> the goddard rocket problem as described in the COPS examples:
>>> 
>>> http://www-unix.mcs.anl.gov/~more/cops/cops3.pdf
>>> 
>>> The good news is, that we managed to find an optimal solution with ipopt
>>> using this implementation (See attached ipopt_cppad_rocket.tgz).
>>> However, when choosing a discretization of  k=500 we were facing quite
>>> long computation times of ~84 minutes with many iterations, while ~1 GB
>>> of memory was reserved  (see attached ipopt_cppad_rocket_k500.log for
>>> details).
>>> 
>>> We did a comparison to our "in house analytical method" which is:
>>> finding of the derivatives of f and g with an analytical method, then
>>> calculate the values of the Jacobian and Hessian for every
>>> discretization step. Using this method we needed only ~5 seconds and
>>> less iterations to find a solution for a discretization of k=500. Here
>>> around 100 MB memory were needed (see attached analytical_k500.log for
>>> details).
>>> 
>>> A while ago we did some runs with AMPL and/or OS on the goodard rocket
>>> problem and experienced run times of < 1 minute for a discretization of
>>> k=500. All calculation times we give are based on ubuntu, Intel Core 2
>>> Duo 2.6 Ghz, 3 GB RAM.
>>> 
>>> 
.
.
.